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Meta Platforms is working on a new artificial-intelligence system intended to be as powerful as the most advanced model offered by OpenAI, the Wall Street Journal reported on Sunday, citing people familiar with the matter.

The Facebook parent is aiming for its new AI model to be ready next year, the Journal said, adding it will be several times more powerful than its commercial version dubbed Llama 2.

Llama 2 is Meta’s open source AI language model launched in July, and distributed by Microsoft’s cloud Azure services to compete with OpenAI’s ChatGPT and Google’s Bard.

The Association for Computing Machinery has just put out the finalists for the Gordon Bell Prize award that will be given out at the SC23 supercomputing conference in Denver, and as you might expect, some of the biggest iron assembled in the world are driving the advanced applications that have their eyes on the prize.

The ACM warns that the final system sizes and final results of the simulations and models run are not yet completed, but we have a look at one of them because the researchers in China’s National Supercomputing Center in Wuxi actually published a paper they will formally released in November ahead of the SC23 conference. That paper, Towards Exascale Computation for Turbomachinery Flows, was run on the “Oceanlite” supercomputing system, which we first wrote about way back in February 2021, that won a Gorden Bell prize in November 2021 for a quantum simulation across 41.9 million cores, and that we speculated the configuration of back in March 2022 when Alibaba Group, Tsinghua University, DAMO Academy, Zhejiang Lab, and Beijing Academy of Artificial Intelligence ran a pretrained machine learning model called BaGuaLu, across more than 37 million cores and 14.5 trillion parameters in the Oceanlite machine.

NASA tossed down a grand challenge nearly a decade ago to do a time-dependent simulation of a complete jet engine, with aerodynamic and heat transfer simulated, and the Wuxi team, with the help of engineering researchers at a number of universities in China, the United States, m and the United Kingdom have picked up the gauntlet. What we found interesting about the paper is that it confirmed many of our speculations about the Oceanlite machine.

The machine generates nearly identical works of art with small discrepancies that make them unique.

Robots or automated systems that are built and programmed to generate different types of artistic creations are referred to as art robots. These robots, which come in a variety of shapes and have different capacities, create artwork using a combination of hardware and software.

Among these machines are certain art robots that are engineered expressly to produce visual art, including drawings and paintings. These robots have the ability to use ink or paint to create an image on a canvas, applying the substances with such tools as pens and paint brushes.

Developed by a spinoff from ETH Zurich, the Ascento Guard is the newest kid on the block for autonomous security robots. It also happens to be very cute.

A Swiss startup called Ascento has recently unveiled its novel and adorable new security robot called the Ascento Guard. An autonomous outdoor security robot’s standout features are its wheeled “legs” and cartoon-esque, almost anthropomorphic “face.”


ETH Zurich/ YouTube.

An autonomous vehicle must rapidly and accurately recognize objects that it encounters, from an idling delivery truck parked at the corner to a cyclist whizzing toward an approaching intersection.

To do this, the vehicle might use a powerful computer vision model to categorize every pixel in a high-resolution image of this scene, so it doesn’t lose sight of objects that might be obscured in a lower-quality image. But this task, known as semantic segmentation, is complex and requires a huge amount of computation when the image has high resolution.

Researchers from MIT, the MIT-IBM Watson AI Lab, and elsewhere have developed a more efficient computer vision model that vastly reduces the computational complexity of this task. Their model can perform semantic segmentation accurately in real-time on a device with limited hardware resources, such as the on-board computers that enable an to make split-second decisions.

An insect-sized robot powered by tiny explosions can crawl, leap and carry a load many times its own weight.

The robot, developed by materials engineer Robert Shepherd at Cornell University in Ithaca, New York, his PhD student Cameron Aubin and their colleagues, is powered by tiny actuators. “The actuator kind of looks like a drum. It’s a hollow cylinder with an elastomeric silicone rubber on the top,” says Aubin.

The researchers used four actuators to drive the robot’s feet. To make the robot jump or crawl, a stream of methane and oxygen is fed into each foot and sparked with electricity from a battery. The resulting reaction between the gases to form water and carbon dioxide releases energy as a small explosion, causing the rubber layer to deform. “That acts sort of like a piston,” Aubin says.

According to the Organisation for Economic Co-operation and Development estimates, transportation accounts for 27 percent of global carbon emissions. Powered by fossil fuels, road-based transportation contributes 80 percent of these emissions and therefore countries are aggressively pushing for the electrification of vehicles. While major advances have been made for passenger cars and air transport, water transport is still lagging. Yara’s new cargo ship might just lead the way.

When Elon Musk announced the team behind his new artificial intelligence company xAI last month, whose mission is reportedly to “understand the true nature of the universe,” it underscored the criticality of answering existential concerns about AI’s promise and peril.

Whether the newly formed company can actually align its behavior to reduce the potential risks of the technology, or whether it’s solely aiming to gain an edge over OpenAI, its formation does elevate important questions about how companies should actually respond to concerns about AI. Specifically: